Build intelligent, connected systems that sense, learn, and act. Master microcontroller programming, edge AI, IoT cloud platforms, and real-time ML inference — used by engineers shaping the future of smart devices worldwide.
IoT engineers with machine learning skills are in surging demand across manufacturing, healthcare, agriculture, smart cities, and consumer electronics. This course takes you from IoT and embedded systems fundamentals all the way through training and deploying ML models on resource-constrained edge devices — using industry-standard platforms and real hardware.
You'll build complete end-to-end systems — programming microcontrollers, wiring sensors, streaming data to the cloud, training ML models, and deploying inference on the edge. By graduation you'll have a portfolio of working smart device projects and the hands-on skills to design intelligent IoT solutions across any industry.
The course is structured into focused modules that build on each other — from electronics and microcontroller programming through to edge AI and cloud IoT platforms. Each module combines theory, guided labs, and a hands-on hardware mini-project.
Understand the architecture of IoT systems — device layers, networking topologies, communication protocols, and how data flows from sensor to cloud.
Get hands-on with the most widely used microcontroller platforms — program Arduino and ESP32 in C/C++ and Python for real embedded applications.
Interface a wide range of sensors and actuators — temperature, humidity, motion, proximity, and more — with real-world circuit design and signal conditioning.
Use Raspberry Pi as a powerful IoT gateway and edge computing node — running Linux, Python scripts, camera modules, and local ML inference.
Train ML models specifically for IoT use cases — anomaly detection, predictive maintenance, gesture recognition, and time-series forecasting on sensor data.
Deploy trained ML models directly on microcontrollers and edge devices — optimising, quantising, and running inference with TensorFlow Lite and Edge Impulse.
Connect devices to enterprise-grade cloud IoT platforms — manage fleets of devices, ingest real-time data streams, and build cloud dashboards.
Collect, store, and analyse sensor data streams at scale — time-series databases, stream processing pipelines, and live IoT dashboards.
Secure IoT deployments end-to-end — device authentication, encrypted communications, firmware update security, and threat modelling for connected systems.
Design systems that process data at the network edge — reducing latency, bandwidth costs, and cloud dependency for real-time IoT applications.
Apply IoT and ML in real industrial contexts — smart manufacturing, precision agriculture, smart city infrastructure, and connected healthcare devices.
Land your first or next IoT engineering role — capstone project guidance, CV writing, GitHub hardware project documentation, and technical interview coaching.
Microcontroller programming — Arduino, ESP32 & Raspberry Pi
Sensor interfacing, circuits & embedded C/Python
Machine learning model training for IoT sensor data
Edge AI & TinyML deployment with TensorFlow Lite
IoT cloud platforms — AWS IoT Core, Azure IoT Hub
Real-time data streaming, storage & Grafana dashboards
IoT security, TLS encryption & secure firmware updates
Industrial IoT protocols & edge computing architecture
Graduates have landed roles at IoT product companies, industrial automation firms, smart city projects, and as independent embedded systems consultants. Here are the roles you'll be qualified for:
Design and build end-to-end connected device systems — from firmware and sensor integration to cloud connectivity and mobile dashboards.
Develop firmware and low-level software for microcontrollers and embedded Linux systems used in consumer, industrial, and automotive products.
Design complete IoT and ML system architectures — selecting hardware platforms, communication stacks, cloud services, and AI inference strategies.
Build and deploy edge computing infrastructure — running ML inference, local data processing, and cloud-sync on resource-constrained edge nodes.
Create intelligent consumer and industrial smart devices — integrating sensors, connectivity, ML capabilities, and OTA update systems.
Deliver IoT solutions independently — smart home systems, industrial monitoring, precision agriculture platforms, and connected healthcare devices.
Electronics and embedded systems enthusiasts who want to add intelligent ML capabilities to their hardware projects
Software engineers interested in crossing over into hardware integration, embedded systems, and IoT development
IoT professionals who already build connected devices and want to add on-device machine learning and edge AI skills
Hardware developers exploring intelligent, autonomous systems for industrial, consumer, or research applications
Innovators and entrepreneurs building smart connected products, from wearables to industrial monitoring systems